Are Data Scientists the answer ?

With each passing day, our world becomes more interconnected — it is projected that by 2020, we will have about 50 Billion connected devices and about 6 Billion of these devices will be smartphones and this means that data volumes will only continue to grow, according to Andrew McAfee — Principal Research Scientist at MIT, we will very soon run out of a metric system to measure all this data we’re generating.

Organizations have been collecting data on their customers for many years — while some have been very successful at monetizing it, a large majority of organizations have this data stored away somewhere in a remote data center or in the cloud. Now as the broader Big Data and Machine Learning discipline gains popularity everyone, rightfully so, wants to create and apply innovative data-driven solutions to business situations. One of the challenges here is the ability to convert all this raw messy data into interesting insights, enter the sexy and mystical Data Scientist — an individual who, as per many job descriptions, is supposed to magically transform the data and analytics practice in organizations.

Very recently a senior executive at a small retail firm contacted one of my colleagues — he wanted to create an analytics department within his company and he was looking to hire a data scientist to lead up this new team and he wanted to know the best avenues to recruit. As we discussed the responsibilities of this new position, it was very clear that this individual would be measured on executing data-driven decisions and the assumption was that the individual would be able to extract insights from data and also influence management decisions. This is a common theme I’ve encountered with my interactions with several Data Science teams over the last year, there are hardly any formal or informal processes that help action on the insights more importantly there is no clear accountability — to understand why this is challenge let’s take a closer look at who these data scientists are and what motivates them.

Technical expertise: the best data scientists typically have deep expertise in some scientific discipline

Curiosity: a desire to go beneath the surface and discover and distill a problem down into a very clear set of hypotheses that can be tested

Storytelling: the ability to use data to tell a story and to be able to communicate it effectively

Cleverness: the ability to look at a problem in different creative ways

To sum it up Data Scientists are curious individuals with technical expertise to extract insights from large volumes of data and also be able to effectively communicate the results. Naturally, their motivations are more inclined towards the science behind the analysis and not so much at impacting and influencing business decisions. So when we start putting Data Scientists in positions where we need to action on the insights we are taking them away from their core motivation of discovering the insights and placing them in gut and instinct driven management structures and teams. One of the biggest reasons for this, according to me, is that Analytics today is still viewed as a technology and data function — this needs to change quickly if we are truly going to democratize analytics across organizations, as Riley Newman, Director of Data Science and the first Data Scientist at airbnb, shares in his article on how they built their Data Science practice:

The foundation upon which a data science team rests is the culture and perception of data elsewhere in the organization, so defining how we think about data has been a prerequisite to ingraining data science in business functions.

(For people who want to understand the aspect of motivation in greater detail I recommend reading this Short history of Data Science to see how the field came into existence.)

The second aspect of this is that we need to think of long term horizons as we create these Data Science or Analytics Departments. Although we are probably looking to create a small team to begin with we need to keep in mind that this team will grow and grow quickly and so we need to ensure that we build out this team, with all the right personas, looking backward from the future state where analytics is democratized. At the most fundamental level we need to look at three aspects: the creators of insights, the consumers who act on these insights and the true unicorns who need to do a bit of both. The creators of insights are your Data Scientists who are able to apply deep statistical and machine learning models to large volumes of data. The consumers are your typical senior executives who may or may not have a background or even believe in data science but have exceptional business acumen and leadership ability. As you can see just from the short descriptions above, the creators and consumers don’t usually speak the same language.

To truly embed data and analytics into the culture of an organization we need to have these unicorns who can bridge the gap between the creators and consumers. These unicorns must be technically sound and data savvy enough to be able to understand the core fundamentals of data science to have conversations and question the assumptions of models built by data scientists while at the same time have the ability and also business domain knowledge to have a healthy debate with the executives to try a new data-driven approach. In addition these individuals will have to politically navigate the organization, as true change agents, to create a data-driven culture. While there is no universally accepted job title for this function, a few popular titles are Decision Scientist and Citizen Data Scientist. Mark Shafer and his team at Disney use the title Management Science Integration Consultant and this business model seems to be working for them. Whatever it is we decide to call this job function we need to acknowledge that there is a real need for individuals with this skill set.

Going back to the question, Data Scientists are definitely part of the answer, but the true solution is for us to start building effective Data Science teams.

If you do not know how to ask the right question, you discover nothing.